Horizontal flipping and random cropping are widely used data augmentation in the ImageNet [1]}. Because the mirror symmetry is intrinsic property among natural objects, the manifold of ImageNet does not have an essential difference with the source domain under horizontal flipping transformation. Similarly, random cropping and resizing can be considered as adjusting the distance and position of the object from the photographer, thus it could not change the manifold of ImageNet as well. However, horizontally flipping is useless and even harmful data augmentation for ImageNet because of no vertical symmetry among most natural objects. Most images are `up-standing' as this is how humans look at them, which is implicit bias introduced from the photographer. Vertically flipping the source domain of ImageNet yields an entirely different data domain, a `down-standing' domain. We defined such augmentation as Domain Augmentaion. Hence, for a specific dataset, the two sets of transformations used in data augmentation and domain augmentation are mutually-exclusive. Disregarding such differences of domains during training would degrade convergence.
